Physical Sciences and Mathematics Commons^™

Binary Text Classification Using Genetic Programming With Crossover-Based Oversampling For Imbalanced Datasets, Mona Aljero, Nazi̇fe Di̇mi̇li̇ler

Turkish Journal of Electrical Engineering and Computer Sciences

It is well known that classifiers trained using imbalanced datasets usually have a bias toward the majority class. In this context, classification models can present a high classification performance overall and for the majority class, even when the performance for the minority class is significantly lower. This paper presents a genetic programming (GP) model with a crossover-based oversampling technique for oversampling the imbalanced dataset for binary text classification. The aim of this study is to apply an oversampling technique to solve the imbalanced issue and improve the performance of the GP model that employed the proposed technique. The proposed technique …

Labeled Modules In Programs That Evolve, Anil K. Saini

Doctoral Dissertations

Multiple methods have been developed for Inductive Program Synthesis, i.e., synthesizing programs consistent with a set of input-output examples. One such method is genetic programming, which searches for programs with desirable properties from the space of all possible programs through an iterated process of variation and selection that is inspired by natural evolution. Genetic programming has been successful in solving problems from multiple domains. These problems are often challenging because of the range of data types and control structures they require to be solved. Nonetheless, there are many programming problems that are routinely solved by human programmers that cannot be …

Survey Of Evolutionary Behavior Tree Algorithm, Yang Jie, Zhang Qi, Junjie Zeng, Quanjun Yin

Journal of System Simulation

Abstract: Evolutionary behavior tree method is an agent behavior modeling method which uses evolutionary algorithm to generate and optimize behavior tree model. Based on the background knowledge of behavior tree and evolutionary algorithm, three kinds of evolutionary behavior tree algorithms based on genetic programming, grammar evolution and hybrid algorithm as well as corresponding improved algorithms are described, and the advantages and disadvantages of different algorithms are analyzed and compared. The specific applications of evolutionary behavior tree in combat simulation, game artificial intelligence, robotics and other fields are summarized. The future development trends of evolutionary behavior tree are proposed and discussed …

An Evolutionary-Based Image Classification Approach Through Facial Attributes, Seli̇m Yilmaz, Cemi̇l Zalluhoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

With the recent developments in technology, there has been a significant increase in the studies on analysisof human faces. Through automatic analysis of faces, it is possible to know the gender, emotional state, and even theidentity of people from an image. Of them, identity or face recognition has became the most important task whichhas been studied for a long time now as it is crucial to take measurements for public security, credit card verification,criminal identification, and the like. In this study, we have proposed an evolutionary-based framework that relies ongenetic programming algorithm to evolve a binary- and multilabel image classifier …

Automatic Discovery Method Of Dynamic Job Shop Dispatching Rules Based On Hyper-Heuristic Genetic Programming, Suyu Zhang, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: The dynamic job shop has the uncertainty of resource state and the randomness of tasks,so it is difficult to find the common dispatching rules applicable to a variety of complex production scenarios.A method for automatic discovery of dynamic shop dispatching rules based on Hyper-Heuristic genetic programming is proposed,with makespan and average weighted tardiness as the optimization goals,is improved by using the automatic discovery of machine sequencing rules and the dynamic adaptability of workshop scheduling under different production scenarios.Through the semantic analysis of dispatching rules,the function of terminators on different optimization objectives is analyzed.The experiment result shows that …

Simulated Experince Evaluation In Developing Multi-Agent Coordination Graphs, Andrew J. Watson

Theses and Dissertations

Cognitive science has proposed that a way people learn is through self-critiquing by generating 'what-if' strategies for events (simulation). It is theorized that people use this method to learn something new as well as to learn more quickly. This research adds this concept to a graph-based genetic program. Memories are recorded during fitness assessment and retained in a global memory bank based on the magnitude of change in the agent’s energy and age of the memory. Between generations, candidate agents perform in simulations of the stored memories. Candidates that perform similarly to good memories and differently from bad memories are …

Evolved Parameterized Selection For Evolutionary Algorithms, Samuel Nathan Richter

Masters Theses

"Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual's genes survive, typically based on fitness. Various conventional fitness based selection functions exist, each providing a unique method of selecting individuals based on their fitness, fitness ranking within the population, and/or various other factors. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance is likely to be obtained by tuning an EA's selection …

Advanced Techniques For Improving Canonical Genetic Programming, Adam Tyler Harter

Masters Theses

"Genetic Programming (GP) is a type of Evolutionary Algorithm (EA) commonly employed for automated program generation and model identification. Despite this, GP, as most forms of EA's, is plagued by long evaluation times, and is thus generally reserved for highly complex problems. Two major impacting factors for the runtime are the heterogeneous evaluation time for the individuals and the choice of algorithmic primitives. The first paper in this thesis utilizes Asynchronous Parallel Evolutionary Algorithms (APEA) for reducing the runtime by eliminating the need to wait for an entire generation to be evaluated before continuing the search. APEA is applied to …

Evolution Of Network Enumeration Strategies In Emulated Computer Networks, Sean Harris, Eric Michalak, Kevin Schoonover, Adam Gausmann, Hannah Reinbolt, Joshua Herman, Daniel R. Tauritz, Chris Rawlings, Aaron Scott Pope

Computer Science Faculty Research & Creative Works

Successful attacks on computer networks today do not often owe their victory to directly overcoming strong security measures set up by the defender. Rather, most attacks succeed because the number of possible vulnerabilities are too large for humans to fully protect without making a mistake. Regardless of the security elsewhere, a skilled attacker can exploit a single vulnerability in a defensive system and negate the benefits of those security measures. This paper presents an evolutionary framework for evolving attacker agents in a real, emulated network environment using genetic programming, as a foundation for coevolutionary systems which can automatically discover and …

Automated Design Of Network Security Metrics, Aaron Scott Pope, Daniel R. Tauritz, Robert Morning, Alexander D. Kent

Computer Science Faculty Research & Creative Works

Many abstract security measurements are based on characteristics of a graph that represents the network. These are typically simple and quick to compute but are often of little practical use in making real-world predictions. Practical network security is often measured using simulation or real-world exercises. These approaches better represent realistic outcomes but can be costly and time-consuming. This work aims to combine the strengths of these two approaches, developing efficient heuristics that accurately predict attack success. Hyper-heuristic machine learning techniques, trained on network attack simulation training data, are used to produce novel graph-based security metrics. These low-cost metrics serve as …

A Novel Evolutionary Algorithm For Designing Robust Analog Filters, Shaobo Li, Wang Zou, Jianjun Hu

Faculty Publications

Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP) coupled with bond graph modeling. We applied our GP-based robust design (GPRD) algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art …

Genetic Programming-Based Pseudorandom Number Generator For Wireless Identification And Sensing Platform, Cem Kösemen, Gökhan Dalkiliç, Ömer Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

The need for security in lightweight devices such as radio frequency identification tags is increasing and a pseudorandom number generator (PRNG) constitutes an essential part of the authentication protocols that provide security. The main aim of this research is to produce a lightweight PRNG for cryptographic applications in wireless identification and sensing platform family devices, and other related lightweight devices. This PRNG is produced with genetic programming methods using entropy calculation as the fitness function, and it is tested with the NIST statistical test suite. Moreover, it satisfies the requirements of the EPCGen2 standards.

Automated Feature Engineering For Deep Neural Networks With Genetic Programming, Jeff T. Heaton

CCE Theses and Dissertations

Feature engineering is a process that augments the feature vector of a machine learning model with calculated values that are designed to enhance the accuracy of a model’s predictions. Research has shown that the accuracy of models such as deep neural networks, support vector machines, and tree/forest-based algorithms sometimes benefit from feature engineering. Expressions that combine one or more of the original features usually create these engineered features. The choice of the exact structure of an engineered feature is dependent on the type of machine learning model in use. Previous research demonstrated that various model families benefit from different types …

Enhancing Automated Program Repair With Deductive Verification, Xuan-Bach D. Le, Quang Loc Le, David Lo, Claire Le Goues

Research Collection School Of Computing and Information Systems

Automated program repair (APR) is a challenging process of detecting bugs, localizing buggy code, generating fix candidates and validating the fixes. Effectiveness of program repair methods relies on the generated fix candidates, and the methods used to traverse the space of generated candidates to search for the best ones. Existing approaches generate fix candidates based on either syntactic searches over source code or semantic analysis of specification, e.g., test cases. In this paper, we propose to combine both syntactic and semantic fix candidates to enhance the search space of APR, and provide a function to effectively traverse the search space. …

A Study Of The Impact Of Interaction Mechanisms And Population Diversity In Evolutionary Multiagent Systems, Sadat U. Chowdhury

Dissertations, Theses, and Capstone Projects

In the Evolutionary Computation (EC) research community, a major concern is maintaining optimal levels of population diversity. In the Multiagent Systems (MAS) research community, a major concern is implementing effective agent coordination through various interaction mechanisms. These two concerns coincide when one is faced with Evolutionary Multiagent Systems (EMAS).

This thesis demonstrates a methodology to study the relationship between interaction mechanisms, population diversity, and performance of an evolving multiagent system in a dynamic, real-time, and asynchronous environment. An open sourced extensible experimentation platform is developed that allows plug-ins for evolutionary models, interaction mechanisms, and genotypical encoding schemes beyond the one …

Evolving Spatially Aggregated Features For Regional Modeling And Its Application To Satellite Imagery, Sam Kriegman

Graduate College Dissertations and Theses

Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the statistical learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods …

Speciation-Based Genetic Algorithm In Analog Circuit Design, Hasari̇ Karci̇, Gülay Tohumoğlu, Ari̇f Nacaroğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a speciation procedure that improves the local search capability of the genetic algorithm in analog circuit design. There is no need for additional circuit simulation in order to apply this procedure. The procedure is tested in Gaussian, sigmoid, cube, and square circuit design problems. Two sets of 125 simulations with the same seed values are performed for each problem using both the proposed procedure and the canonical genetic algorithm. The simulation results show that the method is statistically better than the canonical genetic algorithm, which suffers from bad locality. The effects of the population size and speciation …

A Cooperative Coevolution Framework For Parallel Learning To Rank, Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma

Research Collection School Of Computing and Information Systems

We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. Extensive experiments on benchmark datasets in …

General Program Synthesis From Examples Using Genetic Programming With Parent Selection Based On Random Lexicographic Orderings Of Test Cases, Thomas Helmuth

Doctoral Dissertations

Software developers routinely create tests before writing code, to ensure that their programs fulfill their requirements. Instead of having human programmers write the code to meet these tests, automatic program synthesis systems can create programs to meet specifications without human intervention, only requiring examples of desired behavior. In the long-term, we envision using genetic programming to synthesize large pieces of software. This dissertation takes steps toward this goal by investigating the ability of genetic programming to solve introductory computer science programming problems. We present a suite of 29 benchmark problems intended to test general program synthesis systems, which we systematically …

Reverse Engineering The Human Brain: An Evolutionary Computation Approach To The Analysis Of Fmri, Nicholas Allgaier

Graduate College Dissertations and Theses

The field of neuroimaging has truly become data rich, and as such, novel analytical methods capable of gleaning meaningful information from large stores of imaging data are in high demand. Those methods that might also be applicable on the level of individual subjects, and thus potentially useful clinically, are of special interest. In this dissertation we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI (functional Magnetic Resonance Imaging) from a 242-subject subset of the IMAGEN project, a European study of risk-taking behavior in adolescents that includes longitudinal …

Symbolic Regression Of Crop Pest Forecasting Using Genetic Programming, Basim Alhadidi, Alaa Alafeef, Heba Al-Hiari

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we propose and evaluate a mathematical model that describes the reported data on crop pests to get an accurate prediction of production costs, food safety, and the protection of the environment. Meteorological factors are not the only things that affect a bumper harvest; it is also affected by crop plant diseases and insect pests. Studies show that relying solely on the naked-eye observations of experts to forecast well-planned agriculture is not always sufficient to achieve effective control. Providing fast, automatic, cheap, and accurate artificial intelligence-based solutions for that task can be of great realistic significance. The proposed …

A Review Of Procedures To Evolve Quantum Algorithms, Adrian Gepp, Phil Stocks

Adrian Gepp

There exist quantum algorithms that are more efficient than their classical counterparts; such algorithms were invented by Shor in 1994 and then Grover in 1996. A lack of invention since Grover’s algorithm has been commonly attributed to the non-intuitive nature of quantum algorithms to the classically trained person. Thus, the idea of using computers to automatically generate quantum algorithms based on an evolutionary model emerged. A limitation of this approach is that quantum computers do not yet exist and quantum simulation on a classical machine has an exponential order overhead. Nevertheless, early research into evolving quantum algorithms has shown promise. …

Meta-Genetic Programming: Co-Evolving The Operators Of Variation, Bruce Edmonds

Turkish Journal of Electrical Engineering and Computer Sciences

The standard Genetic Programming approach is augmented by co-evolving the genetic operators. To do this the operators are coded as trees of indefinite length. In order for this technique to work, the language that the operators are defined in must be such that it preserves the variation in the base population. This technique can varied by adding further populations of operators and changing which populations act as operators for others, including itself, thus to provide a framework for a whole set of augmented GP techniques. The technique is tested on the parity problem. The pros and cons of the technique …